The document discusses testers and testing in the context of ChatGPT. It provides an introduction to ChatGPT and discusses how it can be useful for software testers. The document outlines the typical roles and skills of testers, both currently and how they may evolve in the future. It suggests that artificial intelligence like ChatGPT will help enhance how testers do their jobs in the coming years rather than replace testing roles. The conclusion emphasizes the importance for testers to increase their technical skills and learn how to work with AI tools.
2. 21+ Years of
Experience in IT
Passionate
QA/QC Leader
Currently
working at
DBI Inc
Oracle Certified
Associate
Certified Scrum
Master
Advanced Level
Test
Manager(CTAL)-
ISTQB certified
3. ChatGPT – AI –
Introduction
Testers
Testers and their Skills
(Past/Present/Future)
ChatGPT use cases
How ChatGPT can be
useful to Software
Testers
Skills we need to focus
in next Decade
Any topics/comments
for open discussion
Conclusion Contact info
Agenda
4. ChatGPT – AI –
Introduction
Who created ?
What is GPT ?
what is ChatGPT ?
Why ChatGPT ?
How and how much. We can try it now at chat.openai.com/ Free
Microsoft and OpenAI
7. Testers
As a software tester our role is to make sure that software developed by your team
1) meets the requirements
2) functions correctly
Day to Day tasks may include
1) Review requirements
2) Create test plans
3) Writing test cases
4) Executing tests
5) Reporting issues
6) Work with developers to reproduce/troubleshoot issues
7) Participate in design and code reviews
Types of Testers in Industry at very high level
1) Manual Testers
2) Automation Testers
3) Performance Testers
4) Security Testers
16. Testers and their Skills
(Past/Present/Future)
Manual Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
Automation Testing
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
9)Automation using a Tool – No/less coding
10)Automation using a Tool/ Programming – Heavy Coding
Security Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
Performance Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
18. Testers and their Skills
(Past/Present/Future)
Manual Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
Automation Testing
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
9)Automation using a Tool – No/less coding
10)Automation using a Tool/ Programming – Heavy Coding
Security Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
Performance Testing:
1)QA/QC Concepts in SDLC/Agile world
2)Attention to detail
3)Analytical and Problem-solving skills
4)Strong sense of curiosity
5)Communicate effectively with your team and stakeholders
6)Manage your time and workload
7)Functional Expertise
8)Technical Skills according to the Tech stack
19. Skills we need to focus
in next Decade
How Artificial Intelligence Works
22. Conclusion
AI might be everything in future. We are not simply referencing Robots here. AI will
transform the way we are going to work. Whether its helping you draft you emails or
completing in-depth analysis in seconds. With more than 75% businesses going to
use AI tools for everyday business process it is essential to learn how to work with
this technology.
• Increase knowledge base in Technical skills
• Automation is 95% needed for as skill set though only around 60-70 uses it even for
manual jobs
• Learn How AI works and keep up with Available AI tools in the industry and use them to
your convenience.
Past 10 years : Automation will not replace Manual Testing but enhances performance
Next 5 years : AI will not replace QA jobs but will help to do our jobs better
23. Contact info
Email : dasaradh.g@gmail.com
Mobile : 508-264-9780
LinkedIn - https://www.linkedin.com/in/dasaradha-ram-
gadde-5224529/
Ref - https://www.nytimes.com/2023/02/03/technology/chatgpt-openai-artificial-intelligence.html
Ref : kpel965.com
Ref : The New york times
Ref : The Motley Fool
Ref : WSJ
Ref : Detriot Free Press
Credits – ChatGPT/Brad/Google
Credits – Marketoonist.com
Notas do Editor
OpenAI an American artificial intelligence research laboratory created ChatGPT.
Generative Pre-trained Transformer (GPT)
“ChatGPT is a trained model which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admits its mistakes, challenge incorrect premises, and reject inappropriate requests.”
They introduced ChatGPT to get users’ feedback and learn about its strengths and weaknesses.
During the research preview, usage of ChatGPT is free. We can try it now at chat.openai.com
Microsoft and OpenAI have a close partnership, with Microsoft being one of the largest investors in OpenAI.
As part of the partnership, Microsoft agreed to invest $1 billion in OpenAI, and OpenAI agreed to use Microsoft's Azure cloud computing platform to develop and run its large-scale AI models.
The partnership has already yielded several significant results, including the development of GPT-3, one of the most advanced natural language processing AI models to date. Microsoft also recently acquired an exclusive license to GPT-3 and is working to integrate it into its own products and services and calling it Copilot.
Overall, the partnership between Microsoft and OpenAI has the potential to accelerate the development and deployment of AI technologies, and to bring the benefits of AI to a wider range of people and businesses.
two months after its debut, ChatGPT has more than 30 million users and gets roughly five million visits a day, two people with knowledge of the figures said. That makes it one of the fastest-growing software products in memory. (Instagram, by contrast, took nearly a year to get its first 10 million users.) Google declared a “code red” in response to ChatGPT, fast-tracking many of its own A.I. products in an attempt to catch up.
How many of you observed I repeated my 16 slide again with out any changes?
Based on my experience - A Tester can be a Good Developer, but a Developer might not be Good Tester.
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
Additionally, several technologies enable and support AI:
Graphical processing units are key to AI because they provide the heavy compute power that’s required for iterative processing. Training neural networks requires big data plus compute power.
The Internet of Things generates massive amounts of data from connected devices, most of it unanalyzed. Automating models with AI will allow us to use more of it.
Advanced algorithms are being developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.
APIs, or application programming interfaces, are portable packages of code that make it possible to add AI functionality to existing products and software packages. They can add image recognition capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or call out interesting patterns and insights in data.
In summary, the goal of AI is to provide software that can reason on input and explain on output. AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a replacement for humans – and won’t be anytime soon.
https://youtu.be/T2qQGqZxkD0
Testing can show that defects are present, but cannot prove that there are no defects.
Testing reduces the probability of undiscovered defects remaining in the software but, even if no defects are found, testing is not a proof of correctness.
Automation does
Ref - https://www.nytimes.com/2023/02/03/technology/chatgpt-openai-artificial-intelligence.html
ref : kpel965.com
ref : The New york times
ref : The Motley Fool
ref : WSJ
ref : Detriot Free Press
